But MIT has a gerjillion labs, and at the beginning of the year I found myself paralyzed by indecision. The lab experience can take place anywhere outside my area of expertise; my background is in computer science, so the Media Lab and the Lincoln Lab were off-limits to me, but I could still choose from fields as diverse as astronomy, materials science, or bioengineering—really, anywhere I could get a researcher to allow me to nose around his or her lab for a few weeks.

The details of the requirement are loosely defined—all we’re really told is that we need to “spend time” in a lab, so how we spend that time is pretty much up to us and our researcher co-conspirators. Some students take an active role, dissecting mice or titrating chemicals or changing out the core in MIT’s nuclear reactor — whatever their supervisors will let them get away with. Others take the Dian Fossey approach, simply observing scientists in their native habitat. Anyway the thrust of the requirement is that we get a feel for how science actually gets done.

I was drawn toward the science-y looking labs—materials science and chemical engineering, labs behind floor-to-ceiling glass walls on the Infinite Corridor where white lab coat-clad scientists don those thick rubber gloves and reach into frothing metal cylinders. But for a variety of reasons ostensibly related to my thesis research (but actually related to the 14-year-old boy inside me who still has a fascination with guts and brains and other squishy things), I ended up passing on chemistry and materials in favor of Brain and Cognitive Sciences, and landed at John Gabrieli’s creatively named GabLab.

My approach was to volunteer as a test subject, get my noggin scanned, then interview the researchers for a behind-the-scenes look at how their research gets researched. The first several hours of my lab experience clung doggedly to my preconceptions of brain science, consisting primarily of big, noisy machines and cross-sections of heads.

I had never been through an MRI before, and discovered two things. (1) The borehole in an MRI scanner is roughly the same dimensions as a coffin, and it turns out I’m not quite as non-claustrophobic as I thought I was. (2) They’re damn noisy. I was outfitted with foam-rubber earplugs and pads strapped over my ears, but when your head is eight inches from the core of an MRI machine deep in the throes of a diffusion-tensor imaging run, the sensation is not unlike lying on the hood of a Buick doing 75mph off-road.

I performed a few different tasks while in the scanner, all of which centered on pushing one of two buttons; I did this some 500 times over the two-hour session. (I won’t elaborate on these tasks, lest someone read this who ends up a test subject.) Claustrophobia and clatter notwithstanding, the experience was novel and interesting and I came out of it with a neat-o picture of my brain and having made a small but substantive contribution to science.

Taylor Beck, one of my classmates in the GPSW, worked for several years in neuroscience labs, switching eventually to writing because he didn’t particularly enjoy working at the lab bench. When he first told me this I was mystified—I had in my mind a (naïve) picture of neuroscience that was much like materials science or chemical engineering, all titration and rubber gloves and inscrutable machinery, and I found it hard to imagine how that wouldn’t be fun.

But during my interviews with researchers and assistants and technologists after the scan I started to understand Taylor’s perspective a little better. Neuroscience—and, I suspect, many other sciences—doesn’t get done with test tubes and centrifuges, and I don’t think I’ve seen a single white coat during my time at the GabLab. Moreover, John Gabrieli’s laboratory is basically indistinguishable from many of the corporate offices I’ve worked in: cubicles with computers and people in them, offices with glass doors. Just looking at them, you’d have no idea what the lab’s inhabitants were doing.

What they’re doing, though, is manipulating data. While the resolution of a functional MRI scan is fairly low, the scans still produce a vast amount of information—far too much for humans to work with. And neuroscientists work with impossibly complex material: the adult human brain has something like a hundred billion neurons and a hundred trillion connections between those neurons. Instead of forcing research assistants to work in neuroscience sweatshops, laboring night and day over pictures of my brain and searching for the risk/reward centers, the scientists hand the job off to computers.

And they write code for those computers. Lots and lots of code. Programs to normalize data, to correct for twitchy claustrophobic graduate-student test subjects who can’t keep their heads still, programs to find and fix abnormalities, mistakes in the scans, to sort and to search and to order. To build cool graphs and diagrams that are easier for humans to digest than mountains of incomprehensible raw MRI data.

But all this emphasis on computers means that what actually goes on in the lab—outside of the scanning itself—consists of a whole lot of time in meetings talking about things like budgets and planning and scheduling, researchers presenting their work to their colleagues, coders coding , power lunches and water-cooler banter. Pretty much like any other office, except with more brains.

This is not to say that exciting science doesn’t get done in the GabLab—quite the opposite. These scientists are working to understand memory, to find the roots of autism and dyslexia, to learn how we learn, how we understand. But the way this science gets done is far from the traditional picture, and it’s not thrilling to watch. It’s unsurprising, once you stop to think about it, but it also presents a challenge for a science writer. How do you convey the exhilaration of discovery when discovery happens so slowly, and with such a preponderance of tedium?

It’s a problem I struggle with regularly—I’m interested in computers, after all, and computer science belongs to the butts-in-chairs school of research, too. I don’t have a decent answer to this question. But MIT is not a bad place to work on it, and writing blog posts helps. Getting my hands dirty in MRI scanners helps, too, as does tackling other, flashier, subjects outside my normal areas.

So I try my hand at cognitive science and meteorology and archaeology—scientists in khaki shirts, on their knees in the Libyan desert scraping away at thousand-year-old ruins, are not difficult to make interesting. My fellow GPSW students have tackled everything from stomach ulcers to spiders. Check it all out on Scope, the online publication of the GPSW—and check back frequently to see how we’re faring on the tedium-as-excitement problem.

Also, if you’re in the Boston area and are interested in making a contribution to science, you should check out the Brain and Cognitive Science test-subject signup page. They’re always looking for victims. I mean volunteers.